GLM-4.7 vs Qwen3.5 27B vs Qwen3.5 35B-A3B
Too close to call on our weighted score (Qwen3.5 35B-A3B 63, Qwen3.5 27B 61, GLM-4.7 42). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.7
42/100- ECI143.5
- Price$0.60 / $2.20
- Context205K
Alibaba (Qwen)Qwen3.5 27B
61/100- ECI—
- Price$0.30 / $2.40
- Context262K
- Alibaba (Qwen)
Qwen3.5 35B-A3B
63/100- ECI142.5
- Price$0.25 / $2.00
- Context262K
Too close to call
It is close. Our weighted score puts them within 2 points (Qwen3.5 35B-A3B 63/100, Qwen3.5 27B 61/100, GLM-4.7 42/100), so choose by what matters most for your work: Qwen3.5 35B-A3B on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceQwen3.5 35B-A3BQwen3.5 35B-A3B $0.688 · Qwen3.5 27B $0.825 · GLM-4.7 $1.00 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 27B and Qwen3.5 35B-A3BQwen3.5 27B 262,144 · Qwen3.5 35B-A3B 262,144 · GLM-4.7 204,800 tokens
- Widest inputsQwen3.5 27B and Qwen3.5 35B-A3BGLM-4.7: Text · Qwen3.5 27B: Text, Images, Audio, Video · Qwen3.5 35B-A3B: Text, Images, Audio, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.7 | Qwen3.5 27B | Qwen3.5 35B-A3B |
|---|---|---|---|---|
| Price | 50% | 50 | 54 | 58 |
| Inputs & features | 30% | 35 | 90 | 90 |
| Context window | 20% | 32 | 37 | 37 |
| Overall | 100% | 42/100 | 61/100 | 63/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-4.7Z.ai (Zhipu) | Qwen3.5 27BAlibaba (Qwen) | Qwen3.5 35B-A3BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 143.5 (best) | — | 142.5 |
| ECI rank | #84 of 148 (best) | — | #88 of 148 |
| GPQA DiamondGraduate-level science questions | 83.3% | — | 83.5% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 83.3% (best) | — | 70.0% |
| SimpleQA VerifiedShort factual questions | 32.2% | — | — |
| Price per million tokens | |||
| Input | $0.60 | $0.30 | $0.25 (best) |
| Output | $2.20 | $2.40 | $2.00 (best) |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $1.00 | $0.825 | $0.688 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Alibaba API | Official Alibaba API |
| Limits | |||
| Context window | 204,800 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 131,072 tokens (best) | 65,536 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | Yes | Yes |
| Video | No | Yes | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.7 | qwen3.5-27b | qwen3.5-35b-a3b |
| API providers | 20 (best) | 16 | 18 |
| Released | Dec 22, 2025 | Feb 23, 2026 | Feb 23, 2026 |
| Knowledge cutoff | Apr 2025 | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.7$10.40
- Qwen3.5 27B$7.80
- Qwen3.5 35B-A3B$6.50
Which should you choose?
Which is better: GLM-4.7, Qwen3.5 27B or Qwen3.5 35B-A3B?
It is close. Our weighted score puts them within 2 points (Qwen3.5 35B-A3B 63/100, Qwen3.5 27B 61/100, GLM-4.7 42/100), so choose by what matters most for your work: Qwen3.5 35B-A3B on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GLM-4.7, Qwen3.5 27B or Qwen3.5 35B-A3B?
Qwen3.5 35B-A3B is cheaper at $0.25 input / $2.00 output per million tokens (official Alibaba API price). Qwen3.5 27B costs $0.30 input / $2.40 output per million tokens (official Alibaba API price); GLM-4.7 costs $0.60 input / $2.20 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.688 per million tokens for Qwen3.5 35B-A3B versus $0.825 for Qwen3.5 27B (1.2× as much) and $1.00 for GLM-4.7 (1.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.7 has an ECI of 143.5, Qwen3.5 27B has not been scored yet and Qwen3.5 35B-A3B has an ECI of 142.5.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7, Qwen3.5 27B and Qwen3.5 35B-A3B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3.5 27B and Qwen3.5 35B-A3B have the largest context windows (262,144 and 262,144 tokens), against 204,800 for GLM-4.7. Maximum output per response: GLM-4.7 up to 131,072, Qwen3.5 27B up to 65,536, Qwen3.5 35B-A3B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7 accepts text; Qwen3.5 27B accepts text, images, audio and video; Qwen3.5 35B-A3B accepts text, images, audio and video. Qwen3.5 27B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Qwen3.5 27B is the newest, released Feb 23, 2026. Qwen3.5 35B-A3B came out Feb 23, 2026; GLM-4.7 came out Dec 22, 2025. Knowledge cutoff: GLM-4.7 Apr 2025.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
